{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VAE based clusteing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "c:\\users\\admin-karim\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\matplotlib\\__init__.py:1405: UserWarning: \n",
      "This call to matplotlib.use() has no effect because the backend has already\n",
      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "  warnings.warn(_use_error_msg)\n",
      "c:\\users\\admin-karim\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\sklearn\\utils\\linear_assignment_.py:21: DeprecationWarning: The linear_assignment_ module is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from time import time\n",
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "np.random.seed(10)\n",
    "import numpy as np\n",
    "import keras.backend as K\n",
    "from keras.engine.topology import Layer, InputSpec\n",
    "from keras.layers import Dense, Input\n",
    "from keras.models import Model\n",
    "from keras.optimizers import SGD\n",
    "from keras import callbacks\n",
    "from keras.initializers import VarianceScaling\n",
    "from sklearn.cluster import KMeans\n",
    "import metrics\n",
    "\n",
    "from keras.models import Model\n",
    "from keras import backend as K\n",
    "from keras import layers\n",
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Flatten, Reshape, Conv2DTranspose\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "import os \n",
    "from keras.preprocessing.image import load_img\n",
    "import _pickle as cPickle\n",
    "import _pickle \n",
    "import seaborn as sns\n",
    "import sklearn.metrics\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "%matplotlib inline\n",
    "import gzip\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib\n",
    "import os\n",
    "# For plotting graphs via ssh with no display\n",
    "# Ref: https://stackoverflow.com/questions/2801882/generating-a-png-with-matplotlib-when-display-is-undefined\n",
    "matplotlib.use('Agg')\n",
    "from keras.preprocessing.image import load_img\n",
    "from matplotlib import pyplot as plt\n",
    "from numpy import float32\n",
    "from sklearn import metrics\n",
    "from sklearn.cluster.k_means_ import KMeans\n",
    "from sklearn import manifold\n",
    "from sklearn.utils.linear_assignment_ import linear_assignment\n",
    "from sklearn import preprocessing\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load breast biohistology images for convolutional input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadDataset():\n",
    "    data= []\n",
    "    labels =[]\n",
    "    root ='/home/rkarim/Training_data/'\n",
    "\n",
    "    for rootName,dirName,fileNames in os.walk(root):\n",
    "        if(not rootName == root):\n",
    "            for fileName in fileNames:\n",
    "                imgGray = load_img(rootName+'/'+fileName,color_mode='grayscale')\n",
    "                if rootName.split('/')[1] == 'Benign':\n",
    "                    labels+=[0]\n",
    "                elif rootName.split('/')[1]== 'InSitu':\n",
    "                    labels+=[1]\n",
    "                elif rootName.split('/')[1]  == 'Invasive':\n",
    "                    labels+=[2]\n",
    "                else:\n",
    "                    labels+=[3]\n",
    "                  \n",
    "                transformed=transform.resize(np.array(imgGray),(508,508))\n",
    "                data += [transformed.reshape((transformed.shape[0],transformed.shape[1],1))]        \n",
    "          \n",
    "    data = np.stack(data)\n",
    "    labels = np.stack(labels)\n",
    "    #data,labels = shuffle(data,labels,random_state = 0)\n",
    "    \n",
    "    return [data,labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y =  loadDataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
    "     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n",
    "     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, \n",
    "     1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, \n",
    "     1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, \n",
    "     1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, \n",
    "     2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, \n",
    "     2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, \n",
    "     3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, \n",
    "     3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n",
    "\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = x.astype('float32') / 255.\n",
    "#x = np.reshape(x, (len(x), 508, 508))  # adapt this if using `channels_first` image data format\n",
    "data.shape\n",
    "#data = np.reshape(data, (len(data), 508, 508))\n",
    "data = data.reshape((len(data), np.prod(data.shape[1:])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cluster: 4\n",
      "Input shape: (266, 258064)\n",
      "Timestep: 266\n",
      "Data dimension: 258064\n"
     ]
    }
   ],
   "source": [
    "n_clusters = len(np.unique(y))\n",
    "print(\"Number of cluster: \" + str(n_clusters))\n",
    "print(\"Input shape: \" + str(data.shape))\n",
    "\n",
    "print(\"Timestep: \" + str(data.shape[0]))\n",
    "print(\"Data dimension: \" + str(data.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MNIST dataset\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "image_size = x_train.shape[1]\n",
    "original_dim = image_size * image_size\n",
    "x_train = np.reshape(x_train, [-1, original_dim])\n",
    "x_test = np.reshape(x_test, [-1, original_dim])\n",
    "x_train = x_train.astype('float32') / 255\n",
    "x_test = x_test.astype('float32') / 255\n",
    "\n",
    "X = np.concatenate((x_train, x_test))\n",
    "y = np.concatenate((y_train, y_test))\n",
    "\n",
    "# network parameters\n",
    "input_shape = (original_dim, )\n",
    "intermediate_dim = 512\n",
    "batch_size = 128\n",
    "latent_dim = 2\n",
    "epochs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cluster: 10\n",
      "Input shape: (70000, 784)\n",
      "Timestep: 70000\n",
      "Data dimension: 784\n"
     ]
    }
   ],
   "source": [
    "n_clusters = len(np.unique(y))\n",
    "print(\"Number of cluster: \" + str(n_clusters))\n",
    "print(\"Input shape: \" + str(X.shape))\n",
    "\n",
    "print(\"Timestep: \" + str(X.shape[0]))\n",
    "print(\"Data dimension: \" + str(X.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Input, LSTM, RepeatVector\n",
    "from keras.layers.core import Flatten, Dense, Dropout, Lambda\n",
    "from keras.optimizers import SGD, RMSprop, Adam\n",
    "from keras import objectives\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dense\n",
    "from keras.layers import RepeatVector\n",
    "from keras.layers import TimeDistributed\n",
    "from keras.layers import BatchNormalization\n",
    "  \n",
    "x = Input(batch_shape=(batch_size, original_dim))\n",
    "h = Dense(intermediate_dim, activation='relu')(x)\n",
    "bn = BatchNormalization()(h)\n",
    "do = Dropout(0.5)(bn)\n",
    "\n",
    "z_mean = Dense(latent_dim)(do)\n",
    "z_mean = BatchNormalization()(z_mean)\n",
    "z_mean = Dropout(0.5)(z_mean)\n",
    "\n",
    "z_log_sigma = Dense(latent_dim)(do)\n",
    "\n",
    "z_log_var = Dense(latent_dim, name='z_log_var')(x)\n",
    "z_log_var = BatchNormalization()(z_log_var)\n",
    "z_log_var = Dropout(0.5)(z_log_var)\n",
    "\n",
    "def sampling(args):\n",
    "    \"\"\"Reparameterization trick by sampling from an isotropic unit Gaussian.\n",
    "    # Arguments\n",
    "        args (tensor): mean and log of variance of Q(z|X)\n",
    "    # Returns\n",
    "        z (tensor): sampled latent vector\n",
    "    \"\"\"\n",
    "\n",
    "    z_mean, z_log_var = args\n",
    "    batch = K.shape(z_mean)[0]\n",
    "    dim = K.int_shape(z_mean)[1]\n",
    "    # by default, random_normal has mean = 0 and std = 1.0\n",
    "    epsilon = K.random_normal(shape=(batch, dim))\n",
    "    return z_mean + K.exp(0.5 * z_log_var) * epsilon\n",
    "\n",
    "# note that \"output_shape\" isn't necessary with the TensorFlow backend\n",
    "# so you could write `Lambda(sampling)([z_mean, z_log_sigma])`\n",
    "z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_sigma])\n",
    "\n",
    "decoder_h = Dense(512, activation='relu')\n",
    "\n",
    "decoder_mean = Dense(original_dim, activation='sigmoid')\n",
    "h_decoded = decoder_h(z)\n",
    "x_decoded_mean = decoder_mean(h_decoded)\n",
    "\n",
    "# end-to-end autoencoder\n",
    "vae = Model(x, x_decoded_mean)\n",
    "\n",
    "# encoder, from inputs to latent space\n",
    "encoder = Model(x, z_mean)\n",
    "\n",
    "# generator, from latent space to reconstructed inputs\n",
    "decoder_input = Input(shape=(latent_dim,))\n",
    "_h_decoded = decoder_h(decoder_input)\n",
    "_x_decoded_mean = decoder_mean(_h_decoded)\n",
    "generator = Model(decoder_input, _x_decoded_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vae_loss(x, x_decoded_mean):\n",
    "    xent_loss = objectives.binary_crossentropy(x, x_decoded_mean)\n",
    "    kl_loss = - 0.5 * K.mean(1 + z_log_sigma - K.square(z_mean) - K.exp(z_log_sigma), axis=-1)\n",
    "    return xent_loss + kl_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_8 (InputLayer)            (128, 784)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_17 (Dense)                (128, 512)           401920      input_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (128, 512)           2048        dense_17[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (128, 512)           0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_18 (Dense)                (128, 2)             1026        dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (128, 2)             8           dense_18[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (128, 2)             0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_19 (Dense)                (128, 2)             1026        dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_4 (Lambda)               (128, 2)             0           dropout_9[0][0]                  \n",
      "                                                                 dense_19[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_20 (Dense)                multiple             1536        lambda_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_21 (Dense)                multiple             402192      dense_20[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 809,756\n",
      "Trainable params: 808,728\n",
      "Non-trainable params: 1,028\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "autoencoder, encoder = vae, encoder\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pretrain VAE  autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "pretrain_epochs = 100\n",
    "batch_size = 128\n",
    "save_dir = 'results/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder.compile(optimizer='adam', loss=vae_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "69760/69760 [==============================] - ETA: 0s - loss: 0.263 - 11s 161us/step - loss: 0.2630\n",
      "Epoch 2/100\n",
      "69760/69760 [==============================] - 11s 156us/step - loss: 0.2630\n",
      "Epoch 3/100\n",
      "69760/69760 [==============================] - 11s 165us/step - loss: 0.2630\n",
      "Epoch 4/100\n",
      "69760/69760 [==============================] - 11s 160us/step - loss: 0.2630\n",
      "Epoch 5/100\n",
      "69760/69760 [==============================] - 10s 142us/step - loss: 0.2630\n",
      "Epoch 6/100\n",
      "69760/69760 [==============================] - 9s 135us/step - loss: 0.2630\n",
      "Epoch 7/100\n",
      "69760/69760 [==============================] - 9s 129us/step - loss: 0.2630\n",
      "Epoch 8/100\n",
      "69760/69760 [==============================] - 9s 128us/step - loss: 0.2630\n",
      "Epoch 9/100\n",
      "69760/69760 [==============================] - 9s 129us/step - loss: 0.2630\n",
      "Epoch 10/100\n",
      "69760/69760 [==============================] - 9s 134us/step - loss: 0.2630\n",
      "Epoch 11/100\n",
      "69760/69760 [==============================] - 10s 145us/step - loss: 0.2630\n",
      "Epoch 12/100\n",
      "69760/69760 [==============================] - 10s 137us/step - loss: 0.2630\n",
      "Epoch 13/100\n",
      "69760/69760 [==============================] - 9s 133us/step - loss: 0.2630\n",
      "Epoch 14/100\n",
      "69760/69760 [==============================] - 10s 150us/step - loss: 0.2629\n",
      "Epoch 15/100\n",
      "69760/69760 [==============================] - 10s 145us/step - loss: 0.2630\n",
      "Epoch 16/100\n",
      "69760/69760 [==============================] - 9s 132us/step - loss: 0.2629\n",
      "Epoch 17/100\n",
      "69760/69760 [==============================] - 11s 151us/step - loss: 0.2629\n",
      "Epoch 18/100\n",
      "69760/69760 [==============================] - 11s 164us/step - loss: 0.2629\n",
      "Epoch 19/100\n",
      "69760/69760 [==============================] - 10s 141us/step - loss: 0.2629\n",
      "Epoch 20/100\n",
      "69760/69760 [==============================] - 11s 153us/step - loss: 0.2629\n",
      "Epoch 21/100\n",
      "69760/69760 [==============================] - 10s 145us/step - loss: 0.2629\n",
      "Epoch 22/100\n",
      "69760/69760 [==============================] - 11s 161us/step - loss: 0.2629\n",
      "Epoch 23/100\n",
      "69760/69760 [==============================] - 11s 159us/step - loss: 0.2629\n",
      "Epoch 24/100\n",
      "69760/69760 [==============================] - 12s 172us/step - loss: 0.2629\n",
      "Epoch 25/100\n",
      "69760/69760 [==============================] - 12s 165us/step - loss: 0.2629\n",
      "Epoch 26/100\n",
      "69760/69760 [==============================] - 11s 160us/step - loss: 0.2629\n",
      "Epoch 27/100\n",
      "69760/69760 [==============================] - 11s 156us/step - loss: 0.2629\n",
      "Epoch 28/100\n",
      "69760/69760 [==============================] - 9s 135us/step - loss: 0.2629\n",
      "Epoch 29/100\n",
      "69760/69760 [==============================] - 10s 141us/step - loss: 0.2629\n",
      "Epoch 30/100\n",
      "69760/69760 [==============================] - 10s 137us/step - loss: 0.26290s - loss\n",
      "Epoch 31/100\n",
      "69760/69760 [==============================] - 10s 144us/step - loss: 0.2629\n",
      "Epoch 32/100\n",
      "69760/69760 [==============================] - 11s 159us/step - loss: 0.2629\n",
      "Epoch 33/100\n",
      "69760/69760 [==============================] - 10s 146us/step - loss: 0.2629\n",
      "Epoch 34/100\n",
      "69760/69760 [==============================] - 9s 135us/step - loss: 0.2629\n",
      "Epoch 35/100\n",
      "69760/69760 [==============================] - 9s 132us/step - loss: 0.2629\n",
      "Epoch 36/100\n",
      "69760/69760 [==============================] - 9s 132us/step - loss: 0.2629\n",
      "Epoch 37/100\n",
      "69760/69760 [==============================] - 9s 135us/step - loss: 0.2629\n",
      "Epoch 38/100\n",
      "69760/69760 [==============================] - 9s 135us/step - loss: 0.2629\n",
      "Epoch 39/100\n",
      "69760/69760 [==============================] - 9s 127us/step - loss: 0.2629\n",
      "Epoch 40/100\n",
      "69760/69760 [==============================] - 10s 137us/step - loss: 0.2629\n",
      "Epoch 41/100\n",
      "69760/69760 [==============================] - 9s 132us/step - loss: 0.2629\n",
      "Epoch 42/100\n",
      "69760/69760 [==============================] - 9s 131us/step - loss: 0.2629\n",
      "Epoch 43/100\n",
      "69760/69760 [==============================] - 9s 132us/step - loss: 0.2629\n",
      "Epoch 44/100\n",
      "69760/69760 [==============================] - 9s 129us/step - loss: 0.2629\n",
      "Epoch 45/100\n",
      "69760/69760 [==============================] - 9s 129us/step - loss: 0.2629\n",
      "Epoch 46/100\n",
      "69760/69760 [==============================] - 10s 137us/step - loss: 0.2629\n",
      "Epoch 47/100\n",
      "69760/69760 [==============================] - 9s 130us/step - loss: 0.2629\n",
      "Epoch 48/100\n",
      "69760/69760 [==============================] - 9s 128us/step - loss: 0.2629\n",
      "Epoch 49/100\n",
      "69760/69760 [==============================] - 9s 131us/step - loss: 0.2629\n",
      "Epoch 50/100\n",
      "69760/69760 [==============================] - 9s 133us/step - loss: 0.2629\n",
      "Epoch 51/100\n",
      "69760/69760 [==============================] - 9s 130us/step - loss: 0.2629\n",
      "Epoch 52/100\n",
      "69760/69760 [==============================] - 9s 130us/step - loss: 0.2629\n",
      "Epoch 53/100\n",
      "69760/69760 [==============================] - 9s 133us/step - loss: 0.2629\n",
      "Epoch 54/100\n",
      "69760/69760 [==============================] - 9s 132us/step - loss: 0.2629\n",
      "Epoch 55/100\n",
      "69760/69760 [==============================] - 9s 130us/step - loss: 0.2629\n",
      "Epoch 56/100\n",
      "69760/69760 [==============================] - 9s 130us/step - loss: 0.2629\n",
      "Epoch 57/100\n",
      "69760/69760 [==============================] - 9s 129us/step - loss: 0.2629\n",
      "Epoch 58/100\n",
      "69760/69760 [==============================] - 9s 129us/step - loss: 0.2629\n",
      "Epoch 59/100\n",
      "69760/69760 [==============================] - 9s 128us/step - loss: 0.2629\n",
      "Epoch 60/100\n",
      "69760/69760 [==============================] - 9s 128us/step - loss: 0.2629\n",
      "Epoch 61/100\n",
      "69760/69760 [==============================] - 9s 129us/step - loss: 0.2629\n",
      "Epoch 62/100\n",
      "69760/69760 [==============================] - 9s 133us/step - loss: 0.2629: 0s - lo\n",
      "Epoch 63/100\n",
      "69760/69760 [==============================] - 9s 130us/step - loss: 0.2629\n",
      "Epoch 64/100\n",
      "69760/69760 [==============================] - 9s 128us/step - loss: 0.2629\n",
      "Epoch 65/100\n",
      "69760/69760 [==============================] - 9s 130us/step - loss: 0.2629\n",
      "Epoch 66/100\n",
      "69760/69760 [==============================] - 9s 133us/step - loss: 0.2629\n",
      "Epoch 67/100\n",
      "69760/69760 [==============================] - 11s 156us/step - loss: 0.2629\n",
      "Epoch 68/100\n",
      "69760/69760 [==============================] - 12s 166us/step - loss: 0.2629\n",
      "Epoch 69/100\n",
      "69760/69760 [==============================] - 11s 162us/step - loss: 0.2629\n",
      "Epoch 70/100\n",
      "69760/69760 [==============================] - 11s 159us/step - loss: 0.2629\n",
      "Epoch 71/100\n",
      "69760/69760 [==============================] - 11s 159us/step - loss: 0.2629\n",
      "Epoch 72/100\n",
      "69760/69760 [==============================] - 11s 159us/step - loss: 0.2629\n",
      "Epoch 73/100\n",
      "69760/69760 [==============================] - 11s 159us/step - loss: 0.26290s - loss: 0.262\n",
      "Epoch 74/100\n",
      "69760/69760 [==============================] - 11s 158us/step - loss: 0.2629\n",
      "Epoch 75/100\n",
      "69760/69760 [==============================] - 11s 157us/step - loss: 0.26290\n",
      "Epoch 76/100\n",
      "69760/69760 [==============================] - 11s 159us/step - loss: 0.26290s - loss:\n",
      "Epoch 77/100\n",
      "69760/69760 [==============================] - 10s 137us/step - loss: 0.2629\n",
      "Epoch 78/100\n",
      "69760/69760 [==============================] - 10s 137us/step - loss: 0.2629\n",
      "Epoch 79/100\n",
      "69760/69760 [==============================] - 11s 163us/step - loss: 0.2629\n",
      "Epoch 80/100\n",
      "69760/69760 [==============================] - 11s 164us/step - loss: 0.26290s\n",
      "Epoch 81/100\n",
      "69760/69760 [==============================] - 11s 163us/step - loss: 0.2629\n",
      "Epoch 82/100\n",
      "69760/69760 [==============================] - 9s 131us/step - loss: 0.2629\n",
      "Epoch 83/100\n",
      "69760/69760 [==============================] - 10s 142us/step - loss: 0.2629\n",
      "Epoch 84/100\n",
      "69760/69760 [==============================] - 9s 133us/step - loss: 0.2629\n",
      "Epoch 85/100\n",
      "69760/69760 [==============================] - 9s 135us/step - loss: 0.2629\n",
      "Epoch 86/100\n",
      "69760/69760 [==============================] - 9s 135us/step - loss: 0.2629 0s \n",
      "Epoch 87/100\n",
      "69760/69760 [==============================] - 11s 161us/step - loss: 0.2629\n",
      "Epoch 88/100\n",
      "69760/69760 [==============================] - 12s 169us/step - loss: 0.2629\n",
      "Epoch 89/100\n",
      "69760/69760 [==============================] - 9s 134us/step - loss: 0.2629\n",
      "Epoch 90/100\n",
      "69760/69760 [==============================] - 9s 131us/step - loss: 0.2629\n",
      "Epoch 91/100\n",
      "69760/69760 [==============================] - 12s 167us/step - loss: 0.2629\n",
      "Epoch 92/100\n",
      "69760/69760 [==============================] - 10s 142us/step - loss: 0.2629\n",
      "Epoch 93/100\n",
      "69760/69760 [==============================] - 10s 137us/step - loss: 0.2629\n",
      "Epoch 94/100\n",
      "69760/69760 [==============================] - 9s 128us/step - loss: 0.2629\n",
      "Epoch 95/100\n",
      "69760/69760 [==============================] - 9s 136us/step - loss: 0.2629\n",
      "Epoch 96/100\n",
      "69760/69760 [==============================] - 9s 125us/step - loss: 0.2629\n",
      "Epoch 97/100\n",
      "69760/69760 [==============================] - 10s 139us/step - loss: 0.2629\n",
      "Epoch 98/100\n",
      "69760/69760 [==============================] - 10s 149us/step - loss: 0.2629\n",
      "Epoch 99/100\n",
      "69760/69760 [==============================] - 11s 158us/step - loss: 0.2629\n",
      "Epoch 100/100\n",
      "69760/69760 [==============================] - 11s 155us/step - loss: 0.2629\n"
     ]
    }
   ],
   "source": [
    "autoencoder.fit(X[0:69760], X[0:69760], batch_size=batch_size, epochs=pretrain_epochs)\n",
    "autoencoder.save_weights(save_dir+'/conv_ae_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder.load_weights(save_dir+'/conv_ae_weights.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build clustering model with convolutional autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ClusteringLayer(Layer):\n",
    "    \"\"\"\n",
    "    Clustering layer converts input sample (feature) to soft label, i.e. a vector that represents the probability of the\n",
    "    sample belonging to each cluster. The probability is calculated with student's t-distribution.\n",
    "\n",
    "    # Example\n",
    "    ```\n",
    "        model.add(ClusteringLayer(n_clusters=10))\n",
    "    ```\n",
    "    # Arguments\n",
    "        n_clusters: number of clusters.\n",
    "        weights: list of Numpy array with shape `(n_clusters, n_features)` witch represents the initial cluster centers.\n",
    "        alpha: degrees of freedom parameter in Student's t-distribution. Default to 1.0.\n",
    "    # Input shape\n",
    "        2D tensor with shape: `(n_samples, n_features)`.\n",
    "    # Output shape\n",
    "        2D tensor with shape: `(n_samples, n_clusters)`.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, n_clusters, weights=None, alpha=1.0, **kwargs):\n",
    "        if 'input_shape' not in kwargs and 'input_dim' in kwargs:\n",
    "            kwargs['input_shape'] = (kwargs.pop('input_dim'),)\n",
    "        super(ClusteringLayer, self).__init__(**kwargs)\n",
    "        self.n_clusters = n_clusters\n",
    "        self.alpha = alpha\n",
    "        self.initial_weights = weights\n",
    "        self.input_spec = InputSpec(ndim=2)\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        assert len(input_shape) == 2\n",
    "        input_dim = input_shape[1]\n",
    "        self.input_spec = InputSpec(dtype=K.floatx(), shape=(None, input_dim))\n",
    "        self.clusters = self.add_weight((self.n_clusters, input_dim), initializer='glorot_uniform', name='clusters')\n",
    "        if self.initial_weights is not None:\n",
    "            self.set_weights(self.initial_weights)\n",
    "            del self.initial_weights\n",
    "        self.built = True\n",
    "\n",
    "    def call(self, inputs, **kwargs):\n",
    "        \"\"\" student t-distribution, as same as used in t-SNE algorithm.\n",
    "         Measure the similarity between embedded point z_i and centroid µ_j.\n",
    "                 q_ij = 1/(1+dist(x_i, µ_j)^2), then normalize it.\n",
    "                 q_ij can be interpreted as the probability of assigning sample i to cluster j.\n",
    "                 (i.e., a soft assignment)\n",
    "        Arguments:\n",
    "            inputs: the variable containing data, shape=(n_samples, n_features)\n",
    "        Return:\n",
    "            q: student's t-distribution, or soft labels for each sample. shape=(n_samples, n_clusters)\n",
    "        \"\"\"\n",
    "        q = 1.0 / (1.0 + (K.sum(K.square(K.expand_dims(inputs, axis=1) - self.clusters), axis=2) / self.alpha))\n",
    "        q **= (self.alpha + 1.0) / 2.0\n",
    "        q = K.transpose(K.transpose(q) / K.sum(q, axis=1)) # Make sure each sample's 10 values add up to 1.\n",
    "        return q\n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        assert input_shape and len(input_shape) == 2\n",
    "        return input_shape[0], self.n_clusters\n",
    "\n",
    "    def get_config(self):\n",
    "        config = {'n_clusters': self.n_clusters}\n",
    "        base_config = super(ClusteringLayer, self).get_config()\n",
    "        return dict(list(base_config.items()) + list(config.items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "clustering_layer = ClusteringLayer(n_clusters, name='clustering')(encoder.output)\n",
    "model = Model(inputs=encoder.input, outputs=clustering_layer)\n",
    "model.compile(optimizer='adam', loss='kld')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: initialize cluster centers using k-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=n_clusters, n_init=20)\n",
    "y_pred_kmeans = kmeans.fit_predict(encoder.predict(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11252857142857142"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y, y_pred_kmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_last = np.copy(y_pred_kmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.get_layer(name='clustering').set_weights([kmeans.cluster_centers_])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: deep clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#computing an auxiliary target distribution\n",
    "def target_distribution(q):\n",
    "    weight = q ** 2 / q.sum(0)\n",
    "    return (weight.T / weight.sum(1)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = 0\n",
    "index = 0\n",
    "maxiter = 10000\n",
    "update_interval = 100\n",
    "index_array = np.arange(X.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "tol = 0.0001 # tolerance threshold to stop training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter 0: acc = 0.09861, nmi = -0.00000, ari = 0.00000  ; loss= 0.0\n",
      "Iter 100: acc = 0.09861, nmi = -0.00000, ari = 0.00000  ; loss= 0.0\n",
      "delta_label  0.0 < tol  0.0001\n",
      "Reached tolerance threshold. Stopping training.\n"
     ]
    }
   ],
   "source": [
    "for ite in range(int(maxiter)):\n",
    "    if ite % update_interval == 0:\n",
    "        q = model.predict(X, verbose=0)\n",
    "        p = target_distribution(q)  # update the auxiliary target distribution p\n",
    "\n",
    "        # evaluate the clustering performance\n",
    "        y_pred = q.argmax(1)\n",
    "        if y is not None:\n",
    "            acc = np.round(metrics.accuracy_score(y, y_pred), 5)\n",
    "            nmi = np.round(metrics.mutual_info_score(y, y_pred), 5)\n",
    "            ari = np.round(metrics.adjusted_rand_score(y, y_pred), 5)\n",
    "            loss = np.round(loss, 5)\n",
    "            print('Iter %d: acc = %.5f, nmi = %.5f, ari = %.5f' % (ite, acc, nmi, ari), ' ; loss=', loss)\n",
    "\n",
    "        # check stop criterion\n",
    "        delta_label = np.sum(y_pred != y_pred_last).astype(np.float32) / y_pred.shape[0]\n",
    "        y_pred_last = np.copy(y_pred)\n",
    "        if ite > 0 and delta_label < tol:\n",
    "            print('delta_label ', delta_label, '< tol ', tol)\n",
    "            print('Reached tolerance threshold. Stopping training.')\n",
    "            break\n",
    "    idx = index_array[index * batch_size: min((index+1) * batch_size, X.shape[0])]\n",
    "    loss = model.train_on_batch(x=X[idx], y=p[idx])\n",
    "    index = index + 1 if (index + 1) * batch_size <= X.shape[0] else 0\n",
    "\n",
    "model.save_weights(save_dir + '/conv_DEC_model_final.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the clustering model trained weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights(save_dir + '/conv_DEC_model_final.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc = 0.10000, nmi = -0.00000, ari = 0.00000  ; loss= 0.0\n"
     ]
    }
   ],
   "source": [
    "#Eval.\n",
    "\n",
    "q = model.predict(X, verbose=0)\n",
    "p = target_distribution(q)  # update the auxiliary target distribution p\n",
    "\n",
    "# evaluate the clustering performance\n",
    "y_pred = q.argmax(1)\n",
    "if y is not None:\n",
    "    acc = np.round(metrics.accuracy_score(y, y_pred), 2)\n",
    "    nmi = np.round(metrics.mutual_info_score(y, y_pred), 2)\n",
    "    ari = np.round(metrics.adjusted_rand_score(y, y_pred), 2)\n",
    "    loss = np.round(loss, 5)\n",
    "    print('Acc = %.5f, nmi = %.5f, ari = %.5f' % (acc, nmi, ari), ' ; loss=', loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x210ad5a9358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set(font_scale=3)\n",
    "confusion_matrix = sklearn.metrics.confusion_matrix(y, y_pred)\n",
    "\n",
    "plt.figure(figsize=(14, 10))\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\"d\", annot_kws={\"size\": 20});\n",
    "plt.title(\"Confusion matrix\", fontsize=30)\n",
    "plt.ylabel('True label', fontsize=25)\n",
    "plt.xlabel('Clustering label', fontsize=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vis_data(x_train_encoded, y_train, vis_dim, n_predict, n_train, build_anim):\n",
    "    cmap = plt.get_cmap('rainbow', 20)\n",
    "\n",
    "    # 3-dim vis: show one view, then compile animated .gif of many angled views\n",
    "    if vis_dim == 3:\n",
    "        # Simple static figure\n",
    "        fig = plt.figure()\n",
    "        ax = plt.axes(projection='3d')\n",
    "        p = ax.scatter3D(x_train_encoded[:,0], x_train_encoded[:,1], x_train_encoded[:,2], \n",
    "                c=y_train[:n_predict], cmap=cmap, edgecolor='black')\n",
    "        fig.colorbar(p, drawedges=True)\n",
    "        plt.show()\n",
    "\n",
    "        # Build animation from many static figures\n",
    "        if build_anim:\n",
    "            angles = np.linspace(180, 360, 20)\n",
    "            i = 0\n",
    "            for angle in angles:\n",
    "                fig = plt.figure()\n",
    "                ax = plt.axes(projection='3d')\n",
    "                ax.view_init(10, angle)\n",
    "                p = ax.scatter3D(x_train_encoded[:,0], x_train_encoded[:,1], x_train_encoded[:,2], \n",
    "                        c=y_train[:n_predict], cmap=cmap, edgecolor='black')\n",
    "                fig.colorbar(p, drawedges=True)\n",
    "                outfile = 'anim/3dplot_step_' + chr(i + 97) + '.png'\n",
    "                plt.savefig(outfile, dpi=96)\n",
    "                i += 1\n",
    "            call(['convert', '-delay', '50', 'anim/3dplot*', 'anim/3dplot_anim_' + str(n_train) + '.gif'])\n",
    "\n",
    "    # 2-dim vis: plot and colorbar.\n",
    "    elif vis_dim == 2:\n",
    "        plt.scatter(x_train_encoded[:,0], x_train_encoded[:,1], \n",
    "                c=y_train[:n_predict], edgecolor='black', cmap=cmap)\n",
    "        plt.colorbar(drawedges=True)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode a number of MNIST digits, then perform t-SNE dim-reduction.\n",
    "x_train_predict = encoder.predict(X)\n",
    "\n",
    "#print \"Performing t-SNE dimensionality reduction...\"\n",
    "x_train_encoded = TSNE(n_components=2).fit_transform(x_train_predict)\n",
    "#np.save('%sx_%sdim_tnse_%s.npy' % (266, 2, 266), x_train_encoded)\n",
    "#x_train_encoded = np.load(str(n_predict) + 'x_' + str(vis_dim) + 'dim_tnse_' + str(n_train) + '.npy')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x210b899db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize result.\n",
    "train_new = False\n",
    "n_train = 70000\n",
    "predict_new = False\n",
    "n_predict = 70000\n",
    "vis_dim = 2\n",
    "build_anim = False\n",
    "    \n",
    "vis_data(x_train_encoded, y, vis_dim, n_predict, n_train, build_anim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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